Open Source Platforms and Frameworks for Artificial Intelligence and Machine Learning

In recent years Artificial Intelligence (AI) and Machine Learning (ML) have emerged from academic labs and become prominent drivers of innovation in the high-tech industry. The number of jobs in this area has rapidly increased along with research output from industry and the commercialization of that research. It is widely accepted that the changes resulting from these advances will shape society. Diversity in these fields must be increased not only to get people from underrepresented populations into these lucrative jobs but also to have a positive impact by expecting that a more diverse workforce will ensure the fairness of data-driven decisions made by AI and ML algorithms, an issue that has come under scrutiny. The work described here is a result of ongoing efforts to modernize AI and ML courses, and to make its techniques an integral part of the software engineering capstone course at our educational institutions. We report on how the courses and the practice of software engineering have adopted cloud based platforms where we gain the benefit of creating virtual machines and containers for necessary software stacks, the pedagogical benefits of using open-source software that enable blending of markup text, code and output from code within the same document. Information about the choice of software libraries to help students produce code and perform experiments on data at an early stage is an important topic that is included in the discussion. We list of examples of where we have leveraged existing student interest in application of AI and ML, to motivate the work on projects. The defense and intelligence community of the US government are invested in a diverse talent pool in this area. Efforts in specialized certification, and faculty-student visits to government labs are described in this work.